3D multi-scale discriminative network with multi-directional edge loss for prostate zonal segmentation in bi-parametric MR images

Abstract

Accurate and reliable segmentation of the prostate, its inner and surrounding structures in magnetic resonance (MR) images, is of essential importance for image-guided prostate interventions and treatments. Further clinical demand is the automated segmentation of central gland (CG) and peripheral zone (PZ). Although T2-weighted (T2W) images can show prostate tissues more clearly, the lack of contrast between PZ and other surrounding tissues in T2W images increases the difficulty of PZ segmentation. In this context, it is necessary to consider using multi-parametric MR images to obtain complementary information for prostate zonal segmentation. In this paper, we propose a 3D multi-scale discriminative network with pyramid attention module (PAM) and residual refinement block (RRB) for automated and accurate segmentation of CG and PZ using bi-parametric (T2W and apparent diffusion coefficient) MR images. One of the major difficulties in prostate MR image segmentation is the ambiguous edge between the prostate and other surrounding anatomical structures, which is reflected in some specific directions. Therefore, we design a multi-directional edge loss to help the network focus on the multi-directional edge information of foreground areas. For the Prostate Multi-parametric MRI (PROMM) dataset, our proposed model achieved Dice similarity coefficient of 0.908 at CG and 0.785 at PZ. The average boundary distance obtained by our model is 1.397 mm at CG and 3.891 mm at PZ. For the NCI-ISBI dataset, our method greatly improves the Dice similarity coefficient at PZ, reaching 0.806 and achieved the Dice similarity coefficient of 0.901 at CG. The experimental results on two different MR prostate datasets demonstrate that our model is more sensitive to object boundaries and outperforms other state-of-the-art methods. The visualization of feature map activations in PAM shows that the proposed model can capture multi-scale discriminative features effectively.